code: def __init__(self, words, args):
      super(Model, self).__init__()
      self.args = args
      self.n_d = args.d
      self.depth = args.depth
      self.drop = nn.Dropout(args.dropout)
      self.embedding_layer = EmbeddingLayer(self.n_d, words)
      self.n_V = self.embedding_layer.n_V
      if args.lstm:
          self.rnn = nn.LSTM(
              self.n_d, self.n_d, self.depth, dropout=args.rnn_dropout
          )
      else:
          self.rnn = sru.SRU(
              self.n_d,
              self.n_d,
              self.depth,
              dropout=args.rnn_dropout,
              rnn_dropout=args.rnn_dropout,
              use_tanh=0,
              rescale=False,  # make sure the behavior is the same as before
              v1=True,  #
              highway_bias=args.bias,
          )
      self.output_layer = nn.Linear(self.n_d, self.n_V)
      # tie weights
      self.output_layer.weight = self.embedding_layer.embedding.weight
  
      self.init_weights()
  
imports: import sys
  import os
  import argparse
  import time
  import random
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import sru
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(words, args)